An application-focused course on creating intelligent agents that learn through trial and error within dynamic environments. Explores Deep Q-Learning, SARSA, and the Cross-Entropy method to solve complex decision-making problems.
LEARNING_IN_PROGRESS
Learning In Progress
Instructor
Jose Portilla, Pierian Training
Duration
26.5 hours
Platform
Udemy
Status
Learning
Reinforcement Learning with Python
Creating Artificial Neural Networks with TensorFlow
Using TensorFlow to create Convolution Neural Networks for Images
Using OpenAI to work with built-in game environments
Using OpenAI to create your own environments for any problem
Create Artificially Intelligent Agents
Tabular Q-Learning
State–action–reward–state–action (SARSA)
Deep Q-Learning (DQN)
DQN using Convolutional Neural Networks
Cross Entropy Method for Reinforcement Learning
Double DQN
Dueling DQN
